ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Land Use Dynamics
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1640840
Unveiling spatiotemporal evolution and driving factors of ecosystem service value: Interpretable HGB-SHAP machine learning model
Provisionally accepted
- Gannan Normal University, Ganzhou, China
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The ecosystem service value (ESV) is a critical element in the preservation of ecological barriers.The objective of this study is to elucidate the nonlinear correlation between ESV and the key factors that contribute to enhancing the accuracy and reliability of ecosystem service value assessment. In this study, ESV were evaluated based on grid and county scales. Furthermore, the driving factors of ESV were explored using the explainable machine learning method. The findings are as follows: (1) The net ESV of the Gangjiang Upstream Basin (GUB) has undergone a decline from 1990 to 2000, with climate regulation and hydrological regulation collectively accounting for approximately 50% of all functions. (2) A mere 0.69% of the areas exhibited an increase in the level of ESV, while 11.19% demonstrated a decline by 2020, based on the grid scale. The ESV exhibited a slight increase in two counties, while it demonstrated a decrease in the remaining 16 counties at the county scale. The ESV exhibited a substantial positive spatial correlation, manifesting as the presence of considerable high-high and low-low clustering regions. (3) The interpretable machine learning analysis revealed a consistently strong negative correlation between ESV and human activity intensity (HAI), fractional vegetation cover (FVC), and elevation across the entire observed range. In contrast, the soil organic matter (SOC) demonstrated a non-linear, highly significant positive correlation with ESV.
Keywords: Multi-scales, Equivalent factor, Spatial autocorrelation analysis, HGB, Shap
Received: 05 Jun 2025; Accepted: 19 Jul 2025.
Copyright: © 2025 XU, Zhang, QIN and LI. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: XIANGMING XU, Gannan Normal University, Ganzhou, China
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